Central plant control system with computation reduction based on sensitivity analysis

ABSTRACT

Disclosed herein are related to a system, a method, and a non-transitory computer readable storing instructions for operating an energy plant comprised of heating, ventilation and air conditioning (HVAC) devices. In one aspect, the system generates gradient data indicating a gradient of operating performance of the energy plant with respect to values of a plurality of control variables of HVAC devices. The system determines, from the plurality of control variables, a reduced group of control variables of the HVAC devices based on the gradient data. The system determines a set of values of the reduced group of control variables. The system operates the energy plant according to the determined set of values of the reduced group of control variables.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 62/537,811, filed Jul. 27, 2017, which is incorporatedherein by reference in its entirety.

BACKGROUND

The present disclosure relates generally to the operation of a centralplant for serving building thermal energy loads. The present disclosurerelates more particularly to systems and methods for optimizing theoperation of one or more subplants of a central plant.

A heating, ventilation and air conditioning (HVAC) system (also referredto as “a central plant” or “an energy plant” herein) may include varioustypes of equipment configured to serve the thermal energy loads of abuilding or building campus. For example, a central plant may includeHVAC devices such as heaters, chillers, heat recovery chillers, coolingtowers, or other types of equipment configured to provide heating orcooling for the building. Some central plants include thermal energystorage configured to store the thermal energy produced by the centralplant for later use.

A central plant may consume resources from a utility (e.g., electricity,water, natural gas, etc.) to heat or cool a working fluid (e.g., water,glycol, etc.) that is circulated to the building or stored for later useto provide heating or cooling for the building. Fluid conduits typicallydeliver the heated or chilled fluid to air handlers located on therooftop of the building or to individual floors or zones of thebuilding. The air handlers push air past heat exchangers (e.g., heatingcoils or cooling coils) through which the working fluid flows to provideheating or cooling for the air. The working fluid then returns to thecentral plant to receive further heating or cooling and the cyclecontinues.

Controlling the central plant includes determining a set of operatingparameters of the HVAC devices. In particular, some HVAC device operatesaccording to a selected operating parameter from a range of operatingparameters. Examples of the operating parameters include a particularvalue of operating capacity (e.g., 50% capacity) of corresponding HVACdevices. Determining a set of operating parameters includes, for acandidate set of operating parameters, predicting thermodynamic states(e.g., pressure values, temperatures values, mass flow values, etc.) ofdifferent HVAC devices in operation together, and predicting powerconsumption of the central plant based on the predicted thermodynamicstates. By comparing power consumptions of different candidate sets ofoperating parameters, a candidate set with the lowest power consumptionmay be determined as the set of operating parameters.

One conventional approach of predicting thermodynamic states of acentral plant for a candidate set of operating parameters includescomputing the full thermodynamic states by a non-linear solver. However,predicting thermodynamic states of the central plant in a complexarrangement by the non-linear solver is inefficient in terms ofcomputational resources (e.g., processor usage and memory used).Furthermore, predicting thermodynamic states for multiple sets ofoperating parameters, and comparing power consumptions for multiple setsof operating parameters to determine a set of thermodynamic statesrendering lower power consumption through a conventional approach areinefficient and computationally exhaustive.

SUMMARY

Various embodiments disclosed herein are related to a controller for anenergy plant having heating, ventilation, or air conditioning (HVAC)devices. The controller includes a processing circuit comprising aprocessor and memory storing instructions executed by the processor. Theprocessing circuit is configured to generate gradient data indicating agradient of operating performance of the energy plant with respect tovalues of a plurality of control variables of the HVAC devices. Theprocessing circuit is configured to determine, from the plurality ofcontrol variables, a reduced group of control variables of the HVACdevices based on the gradient data. The processing circuit is configuredto determine a set of values of the reduced group of control variables.The processing circuit is configured to operate the HVAC devices of theenergy plant according to the determined set of values of the reducedgroup of control variables.

In one or more embodiments, the control variables include at least acapacity, temperature, or pressure of one or more of the HVAC devices.

In one or more embodiments, the processing circuit is configured todetermine the set of values of the reduced group of control variables bydetermining a range of values of a control variable, to which theoperating performance of the energy plant is insensitive, and excludingthe control variable from the plurality of control variables to obtainthe reduced group of control variables.

In one or more embodiments, the processing circuit is configured tooperate the energy plant according to a predetermined value of theexcluded control variable.

In one or more embodiments, the processing circuit is configured todetermine a local minimum of the operating performance of the energyplant. The processing circuit may determine a range of values of theoperating performance of the energy plant. The range of values of theoperating performance may be within a predetermined amount from thelocal minimum. The processing circuit may determine the range of valuesof the control variable corresponding to the range of values of theoperating performance.

In one or more embodiments, the processing circuit is configured todetermine the range of values of the control variable rendering thegradient below a threshold.

In one or more embodiments, the processing circuit is configured todetermine the set of values of the reduced group of control variables bypredicting states of the HVAC devices using a non-linear optimizer.

In one or more embodiments, the processing circuit is configured todetermine the set of values of the reduced group of control variables byexcluding one or more control variables to which the operatingperformance of the energy plant is insensitive from predicting thestates of the HVAC devices using the non-linear optimizer.

In one or more embodiments, the processing circuit is configured togenerate the gradient data in response to detecting a change in atopology of the HVAC devices.

Various embodiments disclosed herein are related to a method ofoperating an energy plant having heating, ventilation, or airconditioning (HVAC) devices. The method includes generating gradientdata indicating a gradient of operating performance of the energy plantwith respect to values of a plurality of control variables of the HVACdevices. The method includes determining, from the plurality of controlvariables, a reduced group of control variables of the HVAC devicesbased on the gradient data. The method includes determining a set ofvalues of the reduced group of control variables. The method includesoperating the HVAC devices of the energy plant according to thedetermined set of values of the reduced group of control variables.

In one or more embodiments, the control variables include at least acapacity, temperature, or pressure of one or more of the HVAC devices.

In one or more embodiments, determining the set of values of the reducedgroup of control variables includes determining a range of values of acontrol variable, to which the operating performance of the energy plantis insensitive, and excluding the control variable from the plurality ofcontrol variables to obtain the reduced group of control variables.

In one or more embodiments, the method further includes operating theenergy plant according to a predetermined value of the excluded controlvariable.

In one or more embodiments, the method further includes determining alocal minimum of the operating performance of the energy plant. Themethod may further include determining a range of values of theoperating performance of the energy plant. The range of values of theoperating performance may be within a predetermined amount from thelocal minimum. The method may further include determining the range ofvalues of the control variable corresponding to the range of values ofthe operating performance.

In one or more embodiments, the method further includes determining therange of values of the control variable rendering the gradient below athreshold.

In one or more embodiments, the method further includes predictingstates of the HVAC devices using a non-linear optimizer.

In one or more embodiments, the method further includes excluding one ormore control variables, to which the operating performance of the energyplant is insensitive from predicting the states of the HVAC devicesusing the non-linear optimizer.

In one or more embodiments, the method further generating the gradientdata in response to detecting a change in a topology of the HVACdevices.

Various embodiments disclosed herein are related to a non-transitorycomputer readable medium comprising instructions when executed by aprocessor cause the processor to detect a change in a topology of aplurality of heat, ventilation, or air conditioning (HVAC) devices of anenergy plant, determine, from a plurality of control variables of theplurality of HVAC devices, a reduced group of control variables of theplurality of HVAC devices, in response to detecting the change in thetopology of the plurality of HVAC devices, determine a set of values ofthe reduced group of control variables, and operate the plurality ofHVAC devices the energy plant according to the determined set of valuesof the reduced group of control variables.

In one or more embodiments, the instructions when executed by theprocessor further cause the processor to generate gradient dataindicating a gradient of operating performance of the energy plant withrespect to values of the plurality of HVAC devices, in response todetecting the change in the topology of the plurality of HVAC devices.The reduced group of control variables may be determined based on thegradient data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a drawing of a building equipped with an HVAC system,according to some embodiments.

FIG. 2 is a schematic of a waterside system, which can be used as partof the HVAC system of FIG. 1, according to some embodiments.

FIG. 3 is a block diagram illustrating an airside system, which can beused as part of the HVAC system of FIG. 1, according to someembodiments.

FIG. 4 is a block diagram of a central plant controller, according tosome embodiments.

FIG. 5 is a block diagram of an equipment allocator of FIG. 4, accordingto some embodiments.

FIG. 6 illustrates various power consumption plots of the HVAC system,according to some embodiments.

FIG. 7 is an example schematic representation of HVAC system, accordingto some embodiments.

FIG. 8 is another example schematic representation of HVAC system,according to some embodiments.

FIG. 9 is a flow chart illustrating a process for determining operatingparameters of the HVAC system through computation reduction based onsensitivity analysis, according to some embodiments.

FIG. 10 is a flow chart illustrating a process for performingsensitivity analysis, according to some embodiments.

DETAILED DESCRIPTION

Overview

Referring generally to the FIGURES, disclosed herein are systems andmethods for determining a set of operating parameters for operating theHVAC system through disclosed computation reduction based on sensitivityanalysis.

In some embodiments, a central plant controller disclosed hereindetermines sensitivity of different control variables on a predictedperformance of the HVAC system, and identifies, from the controlvariables, a reduced group of the control variables to determineoperating parameters of the control variables rendering improvedperformance of the HVAC system. An operating parameter is also referredto as a value or a set point of a control variable (e.g., 50% ofcapacity, temperature value, pressure value, etc.). In some embodiments,the performance of the HVAC system includes, but is not limited to, apredicted power consumption, an operating speed, amount of energyconsumed, cost, or any combination of them of the HVAC system operatingaccording to operating parameters.

In one approach, the central plant controller generates gradient dataindicating gradient of the performance of the central plant with respectto the control variables. In addition, the central plant controller mayidentify the reduced group of the control variables with gradients abovea predetermined threshold and a remaining group of the control variableswith gradients below the predetermined threshold. Effect of theremaining group of the control variables on the performance of thecentral plant may be insignificant. Hence, the central plant controllermay isolate or omit computation of operating states of HVAC devices ofthe HVAC system associated with the remaining group of controlvariables, when predicting performance of the HVAC system. Accordingly,the central plant controller may compare predicted performances of theHVAC system operating according to different sets of values of thereduced group of the control variables, and determine a set of values ofthe control variables rendering an improved performance of the HVACsystem based on the predicted performances. Moreover, the central plantcontroller may operate the HVAC system according to the determined setof values of the control variables.

Beneficially, the central plant controller improves an operationefficiency of the HVAC system by reducing computation resource fordetermining operating parameters of the HVAC system through sensitivityanalysis disclosed herein. The central plant controller may predictoperating states of the HVAC system operating according to correspondingsets of the reduced group of the operating parameters, irrespective ofHVAC devices of the HVAC system associated with a group of operatingparameters insensitive to the performance of the HVAC system. As aresult, the HVAC system may omit or isolate predicting operating statesof the HVAC devices of the HVAC system insensitive to the performance ofthe HVAC system. Consequently, the central plant controller may identifya set of the operating parameters rendering an improved performance ofthe HVAC system in a computationally efficient manner.

Building and HVAC System

Referring now to FIGS. 1-3, an exemplary HVAC system in which thesystems and methods of the present disclosure can be implemented areshown, according to an exemplary embodiment. While the systems andmethods of the present disclosure are described primarily in the contextof a building HVAC system, it should be understood that the controlstrategies described herein may be generally applicable to any type ofcontrol system.

Referring particularly to FIG. 1, a perspective view of a building 10 isshown. Building 10 is served by a building management system (BMS). ABMS is, in general, a system of devices configured to control, monitor,and manage equipment in or around a building or building area. A BMS caninclude, for example, an HVAC system, a security system, a lightingsystem, a fire alerting system, any other system that is capable ofmanaging building functions or devices, or any combination thereof.

The BMS that serves building 10 includes an HVAC system 100. HVAC system100 can include a plurality of HVAC devices (e.g., heaters, chillers,air handling units, pumps, fans, thermal energy storage, etc.)configured to provide heating, cooling, ventilation, or other servicesfor building 10. For example, HVAC system 100 is shown to include awaterside system 120 and an airside system 130. Waterside system 120 canprovide a heated or chilled fluid to an air handling unit of airsidesystem 130. Airside system 130 can use the heated or chilled fluid toheat or cool an airflow provided to building 10. An exemplary watersidesystem and airside system which can be used in HVAC system 100 aredescribed in greater detail with reference to FIGS. 2-3.

HVAC system 100 is shown to include a chiller 102, a boiler 104, and arooftop air handling unit (AHU) 106. Waterside system 120 can use boiler104 and chiller 102 to heat or cool a working fluid (e.g., water,glycol, etc.) and can circulate the working fluid to AHU 106. In variousembodiments, the HVAC devices of waterside system 120 can be located inor around building 10 (as shown in FIG. 1) or at an offsite locationsuch as a central plant (e.g., a chiller plant, a steam plant, a heatplant, etc.). The working fluid can be heated in boiler 104 or cooled inchiller 102, depending on whether heating or cooling is required inbuilding 10. Boiler 104 can add heat to the circulated fluid, forexample, by burning a combustible material (e.g., natural gas) or usingan electric heating element. Chiller 102 can place the circulated fluidin a heat exchange relationship with another fluid (e.g., a refrigerant)in a heat exchanger (e.g., an evaporator) to absorb heat from thecirculated fluid. The working fluid from chiller 102 and/or boiler 104can be transported to AHU 106 via piping 108.

AHU 106 can place the working fluid in a heat exchange relationship withan airflow passing through AHU 106 (e.g., via one or more stages ofcooling coils and/or heating coils). The airflow can be, for example,outside air, return air from within building 10, or a combination ofboth. AHU 106 can transfer heat between the airflow and the workingfluid to provide heating or cooling for the airflow. For example, AHU106 can include one or more fans or blowers configured to pass theairflow over or through a heat exchanger containing the working fluid.The working fluid can then return to chiller 102 or boiler 104 viapiping 110.

Airside system 130 can deliver the airflow supplied by AHU 106 (i.e.,the supply airflow) to building 10 via air supply ducts 112 and canprovide return air from building 10 to AHU 106 via air return ducts 114.In some embodiments, airside system 130 includes multiple variable airvolume (VAV) units 116. For example, airside system 130 is shown toinclude a separate VAV unit 116 on each floor or zone of building 10.VAV units 116 can include dampers or other flow control elements thatcan be operated to control an amount of the supply airflow provided toindividual zones of building 10. In other embodiments, airside system130 delivers the supply airflow into one or more zones of building 10(e.g., via supply ducts 112) without using intermediate VAV units 116 orother flow control elements. AHU 106 can include various sensors (e.g.,temperature sensors, pressure sensors, etc.) configured to measureattributes of the supply airflow. AHU 106 can receive input from sensorslocated within AHU 106 and/or within the building zone and can adjustthe flow rate, temperature, or other attributes of the supply airflowthrough AHU 106 to achieve set-point conditions for the building zone.

Referring now to FIG. 2, a block diagram of a waterside system 200 isshown, according to an exemplary embodiment. In various embodiments,waterside system 200 can supplement or replace waterside system 120 inHVAC system 100 or can be implemented separate from HVAC system 100.When implemented in HVAC system 100, waterside system 200 can include asubset of the HVAC devices in HVAC system 100 (e.g., boiler 104, chiller102, pumps, valves, etc.) and can operate to supply a heated or chilledfluid to AHU 106. The HVAC devices of waterside system 200 can belocated within building 10 (e.g., as components of waterside system 120)or at an offsite location such as a central plant.

In FIG. 2, waterside system 200 is shown as a central plant having aplurality of subplants 202-212. Subplants 202-212 are shown to include aheater subplant 202, a heat recovery chiller subplant 204, a chillersubplant 206, a cooling tower subplant 208, a hot thermal energy storage(TES) subplant 210, and a cold thermal energy storage (TES) subplant212. Subplants 202-212 consume resources (e.g., water, natural gas,electricity, etc.) from utilities to serve the thermal energy loads(e.g., hot water, cold water, heating, cooling, etc.) of a building orcampus. For example, heater subplant 202 can be configured to heat waterin a hot water loop 214 that circulates the hot water between heatersubplant 202 and building 10. Chiller subplant 206 can be configured tochill water in a cold water loop 216 that circulates the cold waterbetween chiller subplant 206 and the building 10. Heat recovery chillersubplant 204 can be configured to transfer heat from cold water loop 216to hot water loop 214 to provide additional heating for the hot waterand additional cooling for the cold water. Condenser water loop 218 canabsorb heat from the cold water in chiller subplant 206 and reject theabsorbed heat in cooling tower subplant 208 or transfer the absorbedheat to hot water loop 214. Hot TES subplant 210 and cold TES subplant212 can store hot and cold thermal energy, respectively, for subsequentuse.

Hot water loop 214 and cold water loop 216 can deliver the heated and/orchilled water to air handlers located on the rooftop of building 10(e.g., AHU 106) or to individual floors or zones of building 10 (e.g.,VAV units 116). The air handlers push air past heat exchangers (e.g.,heating coils or cooling coils) through which the water flows to provideheating or cooling for the air. The heated or cooled air can bedelivered to individual zones of building 10 to serve the thermal energyloads of building 10. The water then returns to subplants 202-212 toreceive further heating or cooling.

Although subplants 202-212 are shown and described as heating andcooling water for circulation to a building, it is understood that anyother type of working fluid (e.g., glycol, CO2, etc.) can be used inplace of or in addition to water to serve the thermal energy loads. Inother embodiments, subplants 202-212 can provide heating and/or coolingdirectly to the building or campus without requiring an intermediateheat transfer fluid. These and other variations to waterside system 200are within the teachings of the present invention.

Each of subplants 202-212 can include a variety of equipment'sconfigured to facilitate the functions of the subplant. For example,heater subplant 202 is shown to include a plurality of heating elements220 (e.g., boilers, electric heaters, etc.) configured to add heat tothe hot water in hot water loop 214. Heater subplant 202 is also shownto include several pumps 222 and 224 configured to circulate the hotwater in hot water loop 214 and to control the flow rate of the hotwater through individual heating elements 220. Chiller subplant 206 isshown to include a plurality of chillers 232 configured to remove heatfrom the cold water in cold water loop 216. Chiller subplant 206 is alsoshown to include several pumps 234 and 236 configured to circulate thecold water in cold water loop 216 and to control the flow rate of thecold water through individual chillers 232.

Heat recovery chiller subplant 204 is shown to include a plurality ofheat recovery heat exchangers 226 (e.g., refrigeration circuits)configured to transfer heat from cold water loop 216 to hot water loop214. Heat recovery chiller subplant 204 is also shown to include severalpumps 228 and 230 configured to circulate the hot water and/or coldwater through heat recovery heat exchangers 226 and to control the flowrate of the water through individual heat recovery heat exchangers 226.Cooling tower subplant 208 is shown to include a plurality of coolingtowers 238 configured to remove heat from the condenser water incondenser water loop 218. Cooling tower subplant 208 is also shown toinclude several pumps 240 configured to circulate the condenser water incondenser water loop 218 and to control the flow rate of the condenserwater through individual cooling towers 238.

Hot TES subplant 210 is shown to include a hot TES tank 242 configuredto store the hot water for later use. Hot TES subplant 210 can alsoinclude one or more pumps or valves configured to control the flow rateof the hot water into or out of hot TES tank 242. Cold TES subplant 212is shown to include cold TES tanks 244 configured to store the coldwater for later use. Cold TES subplant 212 can also include one or morepumps or valves configured to control the flow rate of the cold waterinto or out of cold TES tanks 244.

In some embodiments, one or more of the pumps in waterside system 200(e.g., pumps 222, 224, 228, 230, 234, 236, and/or 240) or pipelines inwaterside system 200 include an isolation valve associated therewith.Isolation valves can be integrated with the pumps or positioned upstreamor downstream of the pumps to control the fluid flows in watersidesystem 200. In various embodiments, waterside system 200 can includemore, fewer, or different types of devices and/or subplants based on theparticular configuration of waterside system 200 and the types of loadsserved by waterside system 200.

Referring now to FIG. 3, a block diagram of an airside system 300 isshown, according to an exemplary embodiment. In various embodiments,airside system 300 can supplement or replace airside system 130 in HVACsystem 100 or can be implemented separate from HVAC system 100. Whenimplemented in HVAC system 100, airside system 300 can include a subsetof the HVAC devices in HVAC system 100 (e.g., AHU 106, VAV units 116,ducts 112-114, fans, dampers, etc.) and can be located in or aroundbuilding 10. Airside system 300 can operate to heat or cool an airflowprovided to building 10 using a heated or chilled fluid provided bywaterside system 200.

In FIG. 3, airside system 300 is shown to include an economizer-type airhandling unit (AHU) 302. Economizer-type AHUs vary the amount of outsideair and return air used by the air handling unit for heating or cooling.For example, AHU 302 can receive return air 304 from building zone 306via return air duct 308 and can deliver supply air 310 to building zone306 via supply air duct 312. In some embodiments, AHU 302 is a rooftopunit located on the roof of building 10 (e.g., AHU 106 as shown inFIG. 1) or otherwise positioned to receive return air 304 and outsideair 314. AHU 302 can be configured to operate an exhaust air damper 316,mixing damper 318, and outside air damper 320 to control an amount ofoutside air 314 and return air 304 that combine to form supply air 310.Any return air 304 that does not pass through mixing damper 318 can beexhausted from AHU 302 through exhaust air damper 316 as exhaust air322.

Each of dampers 316-320 can be operated by an actuator. For example,exhaust air damper 316 can be operated by actuator 324, mixing damper318 can be operated by actuator 326, and outside air damper 320 can beoperated by actuator 328. Actuators 324-328 can communicate with an AHUcontroller 330 via a communications link 332. Actuators 324-328 canreceive control signals from AHU controller 330 and can provide feedbacksignals to AHU controller 330. Feedback signals can include, forexample, an indication of a current actuator or damper position, anamount of torque or force exerted by the actuator, diagnosticinformation (e.g., results of diagnostic tests performed by actuators324-328), status information, commissioning information, configurationsettings, calibration data, and/or other types of information or datathat can be collected, stored, or used by actuators 324-328. AHUcontroller 330 can be an economizer controller configured to use one ormore control algorithms (e.g., state-based algorithms, extremum seekingcontrol (ESC) algorithms, proportional-integral (PI) control algorithms,proportional-integral-derivative (PID) control algorithms, modelpredictive control (MPC) algorithms, feedback control algorithms, etc.)to control actuators 324-328.

Still referring to FIG. 3, AHU 302 is shown to include a cooling coil334, a heating coil 336, and a fan 338 positioned within supply air duct312. Fan 338 can be configured to force supply air 310 through coolingcoil 334 and/or heating coil 336 and provide supply air 310 to buildingzone 306. AHU controller 330 can communicate with fan 338 viacommunications link 340 to control a flow rate of supply air 310. Insome embodiments, AHU controller 330 controls an amount of heating orcooling applied to supply air 310 by modulating a speed of fan 338.

Cooling coil 334 can receive a chilled fluid from waterside system 200(e.g., from cold water loop 216) via piping 342 and can return thechilled fluid to waterside system 200 via piping 344. Valve 346 can bepositioned along piping 342 or piping 344 to control a flow rate of thechilled fluid through cooling coil 334. In some embodiments, coolingcoil 334 includes multiple stages of cooling coils that can beindependently activated and deactivated (e.g., by AHU controller 330, byBMS controller 366, etc.) to modulate an amount of cooling applied tosupply air 310.

Heating coil 336 can receive a heated fluid from waterside system 200(e.g., from hot water loop 214) via piping 348 and can return the heatedfluid to waterside system 200 via piping 350. Valve 352 can bepositioned along piping 348 or piping 350 to control a flow rate of theheated fluid through heating coil 336. In some embodiments, heating coil336 includes multiple stages of heating coils that can be independentlyactivated and deactivated (e.g., by AHU controller 330, BMS controller366, etc.) to modulate an amount of heating applied to supply air 310.

Each of valves 346 and 352 can be controlled by an actuator. Forexample, valve 346 can be controlled by actuator 354 and valve 352 canbe controlled by actuator 356. Actuators 354-356 can communicate withAHU controller 330 via communications links 358-360. Actuators 354-356can receive control signals from AHU controller 330 and can providefeedback signals to AHU controller 330. In some embodiments, AHUcontroller 330 receives a measurement of the supply air temperature froma temperature sensor 362 positioned in supply air duct 312 (e.g.,downstream of cooling coil 334 and/or heating coil 336). AHU controller330 can also receive a measurement of the temperature of building zone306 from a temperature sensor 364 located in building zone 306.

In some embodiments, AHU controller 330 operates valves 346 and 352 viaactuators 354-356 to modulate an amount of heating or cooling providedto supply air 310 (e.g., to achieve a set-point temperature for supplyair 310 or to maintain the temperature of supply air 310 within aset-point temperature range). The positions of valves 346 and 352 affectthe amount of heating or cooling provided to supply air 310 by heatingcoil 336 or cooling coil 334 and may correlate with the amount of energyconsumed to achieve a desired supply air temperature. AHU controller 330can control the temperature of supply air 310 and/or building zone 306by activating or deactivating coils 334-336, adjusting a speed of fan338, or a combination thereof.

Still referring to FIG. 3, airside system 300 is shown to include a BMScontroller 366 and a client device 368. BMS controller 366 can includeone or more computer systems (e.g., servers, supervisory controllers,subsystem controllers, etc.) that serve as system level controllers,application or data servers, head nodes, or master controllers forairside system 300, waterside system 200, HVAC system 100, and/or othercontrollable systems that serve building 10. BMS controller 366 cancommunicate with multiple downstream building systems or subsystems(e.g., HVAC system 100, a security system, a lighting system, watersidesystem 200, etc.) via a communications link 370 according to like ordisparate protocols (e.g., LON, BACnet, etc.). In various embodiments,AHU controller 330 and BMS controller 366 can be separate (as shown inFIG. 3) or integrated. The AHU controller 330 may be a hardware module,a software module configured for execution by a processor of BMScontroller 366, or both.

In some embodiments, AHU controller 330 receives information (e.g.,commands, set points, operating boundaries, etc.) from BMS controller366 and provides information (e.g., temperature measurements, valve oractuator positions, operating statuses, diagnostics, etc.) to BMScontroller 366. For example, AHU controller 330 can provide BMScontroller 366 with temperature measurements from temperature sensors362-364, equipment on/off states, equipment operating capacities, and/orany other information that can be used by BMS controller 366 to monitoror control a variable state or condition within building zone 306.

Client device 368 can include one or more human-machine interfaces orclient interfaces (e.g., graphical user interfaces, reportinginterfaces, text-based computer interfaces, client-facing web services,web servers that provide pages to web clients, etc.) for controlling,viewing, or otherwise interacting with HVAC system 100, its subsystems,and/or devices. Client device 368 can be a computer workstation, aclient terminal, a remote or local interface, or any other type of userinterface device. Client device 368 can be a stationary terminal or amobile device. For example, client device 368 can be a desktop computer,a computer server with a user interface, a laptop computer, a tablet, asmartphone, a PDA, or any other type of mobile or non-mobile device.Client device 368 can communicate with BMS controller 366 and/or AHUcontroller 330 via communications link 372.

Example Climate Control System

Referring to FIG. 4, illustrated is a block diagram of a central plantcontroller 410, according to some embodiments. In some embodiments, thecentral plant controller 410 is part of the HVAC system 100 of FIG. 1.Alternatively, the central plant controller 410 is coupled to the HVACsystem 100 through a communication link. The central plant controller410 may be the AHU controller 330 of FIG. 3, or a combination of the BMScontroller 366 and the AHU controller 330 of FIG. 3. In oneconfiguration, the central plant controller 410 includes a communicationinterface 415, and a processing circuit 420. These components operatetogether to determine a set of operating parameters for operatingvarious HVAC devices of the HVAC system 100. In some embodiments, thecentral plant controller 410 includes additional, fewer, or differentcomponents than shown in FIG. 4.

The communication interface 415 facilitates communication of the centralplant controller 410 with other HVAC devices (e.g., heaters, chillers,air handling units, pumps, fans, thermal energy storage, etc.). Thecommunication interface 415 can be or include wired or wirelesscommunications interfaces (e.g., jacks, antennas, transmitters,receivers, transceivers, wire terminals, etc.). In various embodiments,communications via the communication interface 415 can be direct (e.g.,local wired or wireless communications) or via a communications network(e.g., a WAN, the Internet, a cellular network, etc.). For example, thecommunication interface 415 can include an Ethernet/USB/RS232/RS485 cardand port for sending and receiving data through a network. In anotherexample, the communication interface 415 can include a Wi-Fi transceiverfor communicating via a wireless communications network. In anotherexample, the communication interface 415 can include cellular or mobilephone communication transceivers.

The processing circuit 420 is a hardware circuit executing instructionsto determine a set of parameters for operating HVAC devices of the HVACsystem 100. In one embodiment, the processing circuit 420 includes aprocessor 425, and memory 430 storing instructions (or program code)executable by the processor 425. The memory 430 may be anynon-transitory computer readable medium. In one embodiment, theinstructions executed by the processor 425 cause the processor 425 toform software modules including a high level optimizer 440, and a lowlevel optimizer 450. The high level optimizer 440 may determine how todistribute thermal energy loads across HVAC devices (e.g., subplants,chillers, heaters, valves, etc.) for each time step in the predictionwindow, for example, to minimize the cost of energy consumed by the HVACdevices. The low level optimizer 450 may determine how to operate eachsubplant according to the thermal energy loads determined by the highlevel optimizer 440. In other embodiments, the processor 425 and thememory 430 may be omitted, and the high level optimizer 440 and the lowlevel optimizer 450 may be implemented as hardware modules by areconfigurable circuit (e.g., field programmable gate array (FPGA)), anapplication specific integrated circuit (ASIC), or any circuitries, or acombination of software modules and hardware modules.

In one implementation, the high level optimizer 440 determines thermalenergy loads of HVAC devices of the HVAC system 100, and generates Qallocation data 442 indicating the determined thermal energy loads. Thehigh level optimizer 440 may provide the Q allocation data 442 to thelow level optimizer 450. In return, the high level optimizer 440 mayreceive, from the low level optimizer 450, operating parameter and powerestimation data 448 indicating a set of operating parameters to operateHVAC devices of the HVAC system 100, predicted power consumptions whenoperating the HVAC system 100 according to the set of operatingparameters, or both. Based on the operating parameter and powerestimation data 448, the high level optimizer 440 can operate the HVACsystem 100 accordingly or generate different Q allocation data 442 forfurther optimization. The high level optimizer 440 and the low leveloptimizer 450 may operate together online in real time, or offline atdifferent times.

In one or more embodiments, the high level optimizer 440 includes anasset allocator 445 that determines a distribution of thermal energyloads of the HVAC devices of the HVAC system 100 based on a predictedthermal energy load of the HVAC system 100. In some embodiments, theasset allocator 445 determines the optimal load distribution byminimizing the total operating cost of HVAC system 100 over theprediction time window. In one aspect, given a predicted thermal energyload {circumflex over (l)}_(k) and utility rate information receivedthrough a user input or automatically determined by a scheduler (notshown), the asset allocator 445 may determine a distribution of thepredicted thermal energy load {circumflex over (l)}_(k) across subplantsto minimize the cost. The asset allocator 445 generates the Q allocationdata 442 indicating the predicted loads {circumflex over (l)}_(k) ofdifferent HVAC devices of the HVAC system 100 and provides the Qallocation data 442 to the low level optimizer 450.

In some embodiments, distributing thermal energy load includes causingTES subplants to store thermal energy during a first time step for useduring a later time step. Thermal energy storage may advantageouslyallow thermal energy to be produced and stored during a first timeperiod when energy prices are relatively low and subsequently retrievedand used during a second time period when energy prices are relativelyhigh. The high level optimization may be different from the low leveloptimization in that the high level optimization has a longer timeconstant due to the thermal energy storage provided by TES subplants.The high level optimization may be described by the following equation:θ_(HL)*=argmin_(θ) _(HL) J _(HL)(θ_(HL))  Eq. (1)where θ_(HL)* contains the optimal high level decisions (e.g., theoptimal load {dot over (Q)} for each of subplants) for the entireprediction period and J_(HL) is the high level cost function.

To find the optimal high level decisions θ_(HL)*, the asset allocator445 may minimize the high level cost function J_(HL). The high levelcost function J_(HL) may be the sum of the economic costs of eachutility consumed by each of subplants for the duration of the predictiontime period. For example, the high level cost function J_(HL) may bedescribed using the following equation:J _(HL)(θ_(HL))=Σ_(k=1) ^(n) ^(h) Σ_(i=1) ^(n) ^(s) [Σ_(j=1) ^(n) ^(u) t_(s) ·c _(jk) u _(jik)(θ_(HL))]  Eq. (2)where n_(h) is the number of time steps k in the prediction time period,n_(s) is the number of subplants, t_(s) is the duration of a time step,c_(jk) is the economic cost of utility j at a time step k of theprediction period, and u_(jik) is the rate of use of utility j bysubplant i at time step k. In some embodiments, the cost function J_(HL)includes an additional demand charge term such as:w _(d) c _(demand) max_(n) _(h) (u _(elec)(θ_(HL)),u _(max,ele))  Eq.(3)where w_(d) is a weighting term, c_(demand) is the demand cost, and themax( ) term selects the peak electricity use during the applicabledemand charge period.

In some embodiments, the high level optimization performed by the highlevel optimizer 440 is the same or similar to the high leveloptimization process described in U.S. patent application Ser. No.14/634,609 filed Feb. 27, 2015 and titled “High Level Central PlantOptimization,” which is incorporated by reference herein.

The low level optimizer 450 receives the Q allocation data 442 from thehigh level optimizer 440, and determines operating parameters (e.g.,values of capacities) of the HVAC devices of the HVAC system 100. In oneor more embodiments, the low level optimizer 450 includes an equipmentallocator 460, a state predictor 470, and a power estimator 480.Together, these components operate to determine a set of operatingparameters, for example, rendering reduced power consumption of the HVACsystem 100 for a given set of thermal energy loads indicated by the Qallocation data 442, and generate operating parameter data indicatingthe determined set of operating parameters. Particularly, the low leveloptimizer 450 determines the set of operating parameters based onsensitivity of the operating parameters on a performance of the HVACsystem 100. In some embodiments, the low level optimizer 450 includesdifferent, more, or fewer components, or includes components indifferent arrangements than shown in FIG. 4.

In one configuration, the equipment allocator 460 receives the Qallocation data 442 from the high level optimizer 440, and generatescandidate operating parameter data 462 indicating a set of candidateoperating parameters of HVAC devices of the HVAC system 100. The statepredictor 470 receives the candidate operating parameter data 462 andpredicts thermodynamic states of the HVAC system 100 at variouslocations for the set of candidate operating parameters. The statepredictor 470 generates state data 474 indicating the predictedthermodynamic states, and provides the state data 474 to the powerestimator 480. The power estimator 480 predicts, based on the state data474, total power consumed by the HVAC system 100 operating according tothe set of candidate operating parameters, and generates the powerestimation data 482 indicating the predicted power consumption. Theequipment allocator 460 may repeat the process with different sets ofcandidate operating parameters to obtain predicted power consumptions ofthe HVAC system 100 operating according to different sets of candidateoperating parameters, and select a set of operating parameters renderinglower power consumption. The equipment allocator 460 may generate theoperating parameter and power estimation data 448 indicating (i) theselected set of operating parameters and (ii) predicted powerconsumption of the power plant when operating according to the selectedset of operating parameters, and provide the operating parameter andpower estimation data 448 to the high level optimizer 440.

The equipment allocator 460 is a component that interfaces with the highlevel optimizer 440. In one aspect, the equipment allocator 460 receivesthe Q allocation data, and determines a candidate set of operatingparameters of HVAC devices of the HVAC system 100. For example, theequipment allocator 460 determines that a first chiller is assigned tooperate with a first range of thermal energy load and a second chilleris assigned to operate with a second range of thermal energy load basedon the Q allocation data. In this example, the equipment allocator 460may determine that operating parameters (e.g., between 30% to 50%capacity) of the first chiller can achieve the first range of thermalenergy load and operating parameters (e.g., between 60˜65% capacity) ofthe second chiller can achieve the second range of thermal energy load.From different combinations of operating parameters of the first chillerand the second chiller, the equipment allocator 460 selects a candidateset of operating parameters (e.g., 45% capacity of the first chiller and60% capacity of the second chiller). Additionally, the equipmentallocator 460 generates the candidate operating parameter data 462indicating the selected candidate set of operating parameters, andprovides the candidate operating parameter data 462 to the statepredictor 470.

The state predictor 470 predicts an operating condition of the HVACsystem 100 based on a set of operating parameters of the HVAC system 100as indicated by the candidate operating parameter data 462. Theoperating condition of the HVAC system 100 includes thermodynamic statesat various locations of the HVAC system 100. Examples of thermodynamicstates include input pressure value, output pressure value, input massflow value, output mass flow value, input enthalpy value, outputenthalpy value, etc. In one approach, predicting thermodynamic states ofthe HVAC system 100 includes applying the set of operating parameters toa linear solver and a non-linear solver. Generally, the non-linearsolver consumes a large amount of resources (e.g., processor threads andstorage capacity) to obtain a solution. In one or more embodiments, thestate predictor 470 reduces a number of unknown thermodynamic states tobe predicted based on schematic arrangements of HVAC devices of the HVACsystem 100, and may further reduce the number of unknown thermodynamicstates to be predicted by propagating known thermodynamic states basedon the operating parameters by performing sensitivity analysis andreducing values of control variables to be determined, as described indetail below with respect to FIGS. 5 through 10. Advantageously, a fewernumber of unknown thermodynamic states can be determined by thenon-linear solver, thereby improving efficiency of predicting thethermodynamic states for the set of operating parameters. The statepredictor 470 generates state data 474 indicating the predictedthermodynamic states for the candidate set of operating parameters, andprovides the state data 474 to the power estimator 480.

The power estimator 480 predicts power consumed by the HVAC system 100based on the state data 474. In one approach, the power estimator 480determines, for each HVAC device, a predicted power consumption based onthermodynamic states (e.g., pressure values, mass flow values, enthalpyvalues, etc.) and an operating parameter (e.g., capacity) of the HVACdevice. In addition, the power estimator 480 may add power consumptionsof the HVAC devices of the HVAC system 100 to obtain a total powerconsumption of the HVAC system 100. The power estimator 480 generatesthe power estimation data 482 indicating the total power consumption ofthe HVAC system 100, power consumption of each HVAC device, or anycombination of them, and provides the power estimation data 482 to theequipment allocator 460.

In some embodiments, the equipment allocator 460 compares predictedpower consumptions of the HVAC system 100 for multiple sets of operatingparameters, and selects a set of operating parameters for operating theHVAC system 100. In one approach, the equipment allocator 460 selects,from the multiple sets of operating parameters, the set of operatingparameters rendering the lowest power consumption. Hence, the HVACsystem 100 operating based on the set of operating parameters determinedby the equipment allocator 460 benefits from reduced power consumption.The equipment allocator 460 may generate the operating parameter andpower estimation data 448 indicating the set of operating parameters tooperate HVAC devices of the HVAC system 100, predicted powerconsumptions when operating the HVAC system 100 according to the set ofoperating parameters, or any combination of them, and provide theoperating parameter and power estimation data 448 to the high leveloptimizer 440.

In some embodiments, the equipment allocator 460 performs sensitivityanalysis to identify a reduced group of control variables of HVACdevices for determining a set of values of the control variablesrendering an improved performance (e.g., lower power consumption) of theHVAC system. The sensitivity analysis herein refers to determiningsensitivity of different control variables of the HVAC devices on theperformance of the HVAC devices. Based on the sensitivity analysis, theequipment allocator 460 may determine that values of a reduced group ofcontrol variables in a first range (e.g., a chiller capacity between0˜40%, and a condenser exit temperature between 90˜100 F) may besensitive to the performance of the HVAC system. The equipment allocator460 may also determine that values of a reduced group of controlvariables in a second range (e.g., a chiller capacity between 40˜60%,and a condenser exit temperature between 50˜70 F) may be insensitive tothe performance of the HVAC system. For another example, the equipmentallocator 460 may also determine that different control variables (e.g.,separate control loop) may be insensitive to the performance of the HVACsystem. The equipment allocator 460 may generate different candidateoperating parameter data 462 indicating different sets of values of thereduced group of control variables sensitive to the performance of theHVAC system, without values of other control variables insensitive tothe performance of the HVAC system. The equipment allocator 460 may alsocause or instruct the state predictor 470 to predict operating states ofcomponents of the HVAC system according to values of the reduced groupof the control variables irrespective of values of other controlvariables of the HVAC system. Thus, the group of control variables ofthe HVAC system insensitive to the operating performance may be excludedfrom predicting the operating states of the components of the HVACsystem. Hence, the state predictor 470 may perform computation for afewer number of unknowns, thereby improving computation efficiency.

Referring to FIG. 5, illustrated is a block diagram of the equipmentallocator 460, according to some embodiments. In one configuration, theequipment allocator 460 includes an equipment selector 510, a candidateoperating parameter generator 520, a sensitivity analyzer 530, a reducedoperating parameter generator 542, and an output operating parametergenerator 540. These components operate together to receive the Qallocation data 442 indicating the determined thermal energy loads,determine a set of operating parameters rendering an improvedperformance of the HVAC system for the determined thermal energy load,and generate operating parameter and power estimation data 448indicating the determined set of operating parameters and correspondingpower consumption of the HVAC system. In some embodiments, the equipmentallocator 460 includes additional, fewer, or different components thanshown in FIG. 5.

The equipment selector 510 is a component that interfaces with the highlevel optimizer 440. In one aspect, the equipment selector 510 receivesthe Q allocation data 442 from the high level optimizer 440, anddetermines a set of operating parameters of the HVAC system according tothe Q allocation data 442. In one implementation, the equipment selector510 stores a look up table indicating a relationship between thermalenergy loads and corresponding sets or ranges of operating parameters ofthe HVAC system. For example, the equipment selector 510 receives the Qallocation data 442 indicating a target thermal energy load of a heaterand a target thermal energy load of a cooler. In this example, theequipment selector 510 may determine that a first range of the operatingparameter of the heater corresponds to the target thermal energy load ofthe heater and a second range of the operating parameter of the heatercorresponds to the target thermal energy load of the cooler based on thelook up table.

The candidate operating parameter generator 520 is a component thatinterfaces with the state predictor 470, and generates candidateoperating parameter data 462 based on the operating parameters of theHVAC system. The candidate operating parameter generator 520 maygenerate the candidate operating parameter data 462 based on initialoperating parameters of the HVAC system determined from the equipmentallocator 460. The candidate operating parameter generator 520 maygenerate additional candidate operating parameter data 462 according toperturbation from the sensitivity analyzer 530. In some embodiments, thecandidate operating parameter generator 520 may generate the candidateoperating parameter data 462 indicating a set of values of a reducedgroup of control variables of HVAC devices, without values of aremaining group of the control variables. The candidate operatingparameter generator 520 may also cause the state predictor 470 toperform computation for predicting operating states of HVAC devicesassociated with the values of the reduced group of the controlvariables, irrespective of values of the remaining control variables ofthe HVAC devices. Values of the remaining control variables of the HVACdevices may be assumed to be turned off or set to a predetermined value.

The sensitivity analyzer 530 is a component that performs sensitivityanalysis of values of control variables on the performance of the HVACsystem. In one implementation, the sensitivity analyzer 530 includes aparameter perturbation generator 534 and a gradient analyzer 538. Inthis configuration, the sensitivity analyzer 530 obtains gradients ofperformance of the HVAC system with respect to the control variables ofthe HVAC system, and determines a group of control variables or a rangeof values of the group of control variables, to which the performance ofthe HVAC system is sensitive. Moreover, the sensitivity analyzer 530determines another group of control variables or a range of values ofthe group of control variables, to which the performance of the HVACsystem is insensitive. In one aspect, the sensitivity analyzer 530performs sensitivity analysis prior to deployment of the HVAC system,during initialization or periodically. In some embodiments, thesensitivity analyzer 530 includes additional, fewer, or differentcomponents than shown in FIG. 5.

The parameter perturbation generator 534 is a component that addsperturbation to values of one or more control variables. In one example,the parameter perturbation generator 534 adds or subtracts a value of acontrol variable by a predetermined amount. Hence, the candidateoperating parameter generator 520 can generate different sets ofoperating parameters (or different sets of values of control variables).Furthermore, predicted performances or power consumptions of the HVACsystem operating according to different sets of operating parameters canbe obtained.

The gradient analyzer 538 is a component that automatically analyzessensitivities of the control variables of the HVAC devices. In oneapproach, the gradient analyzer 538 generates gradients data indicatinggradients of the performances of the HVAC system with respect todifferent control variables. A gradient of performances of the HVACsystem represents a sensitivity of a control variable on the performanceof the HVAC system. Thus, by analyzing gradients of the performances ofthe HVAC system with respect to different control variables,sensitivities of different control variables on the performance of theHVAC system can be determined. Moreover, control variables or a range ofvalues of control variables, to which the performance of the HVAC systemis insensitive, may be omitted or isolated, when determining operatingstates of the HVAC system by the state predictor 470. In one approach,the gradient analyzer 538 automatically determines a range of values ofa control variable associated with a gradient less than a predeterminedthreshold as being the performance of the HVAC system sensitive orinsensitive to particular control variables or ranges of values of thecontrol variables. In another approach, the gradient analyzer 538automatically determines a value of a control variable rendering a localminimum of the performance of the HVAC system, and determines values ofthe performance (e.g., 10% of the local minimum) within a predeterminedamount from the local minimum. The gradient analyzer 538 may determinethat the performance of the HVAC system is insensitive to a range ofvalues of the control variable rendering the determined values of theperformance including the local minimum. Similarly, the gradientanalyzer 538 may automatically determine a range of values of a controlvariable associated with a gradient larger than the predeterminedthreshold, and determine that the performance of the HVAC system issensitive to the determined range of values of the control variable. Inone aspect, gradient analyzer 538 stores ranges of values of controlvariables, to which the performance of the HVAC system is insensitive,and ranges of values of control variables, to which the performance ofthe HVAC system is sensitive.

The reduced operating parameter generator 542 is a component thatobtains a reduced group of control variables. The reduced operatingparameter generator 542 may receive a set of operating parameters fromthe HVAC system from the equipment selector 510, and omit or remove oneor more control variables, to which the performance of the HVAC systemis insensitive. The reduced operating parameter generator 542 may referto the look up table from the gradient analyzer 538, and remove acontrol variable having a value within a range of values of the controlvariable, to which the performance of the HVAC system is insensitive, asindicated by the look up table. The reduced operating parametergenerator 542 may generate the candidate operating parameter data 462indicating the reduced group of control variables, and provide thecandidate operating parameter data 462 to the state predictor 470.

The output operating parameter generator 540 is a component thatdetermines a set of values of control variables for operating the HVACsystem, and provides the operating parameter and power estimation data448 indicating the determined set of values of the control variables andpredicted power consumption. In one example, the output operatingparameter generator 540 determines, from different sets of values ofcontrol variables, the set of values of control variables rendering thelowest power consumption. The output operating parameter generator 540may generate the operating parameter and power estimation data 448including a set of values of reduced group of control variables that aresensitive to the operating performance and predetermined values (e.g.,local minima) of the other group of control variables that areinsensitive to the operating performance. The output operating parametergenerator 540 may add predetermined values (e.g., local minima) ofomitted control variables in the operating parameter and powerestimation data 448.

In one aspect, the sensitivity analyzer 530 performs sensitivityanalysis prior to deployment of the HVAC system, during initialization,periodically, in response to detecting a change in topology of the HVACsystem, or any combination of them. During operation of the energyplant, the reduced operating parameter generator 542 may utilize theresult from the sensitivity analysis to reduce a number of states ofvalues of control variables to be predicted by the state predictor 470.Hence, the amount of computation performed by the state predictor 470may be conserved.

FIG. 6 illustrates various power consumption plots 610, 620, 630, 640 ofthe HVAC system, according to some embodiments. Under differentoperating parameters or operating states, power consumptions may vary asshown in the plots 610, 620, 630, 640. Moreover, gradients of the plots610, 620, 630, 640 may be obtained to determine an isolated region ofvalues of control variables. For example, flatter regions 615, 625, 635,645 of the plots 610, 620, 630, 640 may indicate that the powerconsumption of the HVAC system may be insensitive to ranges of values ofcontrol variables corresponding to the flatter regions. A range ofvalues of control variables with a gradient less than a predeterminedthreshold may be automatically determined, and such control variables orrange of values of control variables may be omitted or isolated whenpredicting operating state of the HVAC system or determining powerconsumption of the HVAC system.

In some embodiments, some components of the equipment allocator 460 maybe performed by a non-linear optimizer (e.g., sequential quadraticprogramming optimizer). The non-linear optimizer may be configured toobtain gradients of the power consumption plots 610, 620, 630, 640 anddetermine local minima 615, 625, 635, 645 corresponding to the lowestpower consumption for a given range of control variables. The equipmentallocator 460 may utilize gradients obtained from the non-linearoptimizer to reduce computational resource.

Referring to FIG. 7, illustrated is an example schematic representationof HVAC system 700, according to some embodiments. The example HVACsystem 700 includes a first set 710 of HVAC devices and a second set 720of HVAC devices. Based on the sensitivity analysis by automaticallyanalyzing gradients, the low level optimizer 450 of FIG. 4 may determinethat the performance of the first set 710 of HVAC devices are sensitiveto leaving evaporator water temperature (LEWT) control variables butinsensitive to the leaving condenser water temperature (LCWT), enteringcondenser water temperature (ECWT) control variables. Similarly, the lowlevel optimizer 450 of FIG. 4 may determine that the performance of thesecond set 720 of HVAC devices are sensitive to LEWT, LCWT, ECWT controlvariables. Assuming that the LEWT control variable is modified orupdated, the low level optimizer 450 may predict operating states of thesecond set 720 of HVAC devices irrespective of the operating states ofthe first set 710 of the HVAC devices.

Referring to FIG. 8, illustrated is another example schematicrepresentation of HVAC system 800, according to some embodiments. Theexample HVAC system 800 includes two separate feedback loops 810, 820operating with independent control variables. The low level optimizer450 of FIG. 4 may determine that the feedback loops 810, 820 areindependent from each other through the sensitivity analysis byautomatically analyzing gradients as disclosed herein. Thus, the lowlevel optimizer 450 may predict operating states of the first feedbackloop 810 irrespective of the second feedback loop 820. Similarly, thelow level optimizer 450 may predict operating states of the secondfeedback loop 820 irrespective of the first feedback loop 810.

Referring to FIG. 9, illustrated is a flow chart illustrating a process900 for determining operating parameters of the HVAC system throughcomputation reduction based on sensitivity analysis, according to someembodiments. The process 900 may be performed by the low level optimizer450 of FIG. 4. In some embodiments, the process 900 may be performed byother entities. In some embodiments, the process 900 may includeadditional, fewer, or different steps than shown in FIG. 9.

The low level optimizer 450 performs equipment selection (step 910). Forexample, the equipment selector 510 receives the Q allocation data 442and determines control variables of one or more HVAC devices accordingto the Q allocation data 442. In one example, the Q allocation data 442specifies a load of a HVAC device, and the low level optimizer 450determines control variables of the HVAC device.

The low level optimizer 450 determines whether sensitivity analysis hasbeen performed before (step 920). If the sensitivity analysis has beenperformed already, the low level optimizer 450 determines whether atopology or a schematic configuration of the HVAC system has beenchanged (step 930). The low level optimizer 450 determines a reducedgroup of control variables of the HVAC system based on sensitivityanalysis performed (step 940). For example, the low level optimizer 450identifies a range of values of control variables sensitive to powerconsumption or other operating performances of the HVAC system.Responsive to determining that the sensitivity analysis has been alreadyperformed and the topology of the HVAC system has not been changed, thelow level optimizer 450 may rely on the sensitivity analysis performedpreviously to determine the reduced group of control variables of theHVAC system. Responsive to determining that the sensitivity analysis hasnot been performed or determining that the topology of the HVAC systemhas been updated, the low level optimizer 450 perform a new sensitivityanalysis (step 935). The low level optimizer 450 may rely on thesensitivity analysis performed in step 935 to determine the reducedgroup of control variables of the HVAC system. Hence, the low leveloptimizer 450 may not perform sensitivity analysis every time whendetermining the operating parameters of the HVAC system.

The low level optimizer 450 may predict operating states of HVAC devicesof the HVAC system according to the reduced group of the controlvariables that are sensitive to power consumption of the HVAC system.The low level optimizer 450 may exclude or isolate predicting operatingstates of other control variables insensitive to the power consumptionof the HVAC system. For each set of values of the reduced group ofcontrol variables, the low level optimizer 450 may predict acorresponding operating performance (e.g., power consumption) of theHVAC system.

The low level optimizer 450 may determine a set of values of controlvariables of the HVAC system by comparing predicted operatingperformances of the HVAC system operating according to different sets ofvalues of the control variables (step 950). For example, the low leveloptimizer 450 may determine a set of values of control variables of theHVAC system rendering lower power consumptions than other sets of valuesof the control variables. The low level optimizer 450 generates theoperating parameter and power estimation data 448 indicating thedetermined set of values of control variables and predicted powerconsumption of the HVAC system, and provides the operating parameter andpower estimation data 448 to the high level optimizer 440. Based on theoperating parameter and power estimation data 448, the HVAC system mayoperate according to the determined operating parameters.

Advantageously, by omitting or isolating control variables of HVACdevices insensitive to the power consumption of the HVAC systemaccording to disclosed sensitivity analysis, a number of unknowns to besolved can be reduced. Hence, computation resource for predictingoperating states of HVAC system and predicting operating performance canbe conserved. For example, determining values of control variables ofthe HVAC system through a conventional approach may take several hours,whereas determining values of control variables of the HVAC systemthrough a disclosed approach may take less than 15 minutes.

Referring to FIG. 10, illustrated is a flow chart illustrating a process900 for performing sensitivity analysis, according to some embodiments.The process 900 may be performed by the low level optimizer 450 of FIG.4. In some embodiments, the process 900 may be performed by otherentities. In some embodiments, the process 900 may include additional,fewer, or different steps than shown in FIG. 10.

The low level optimizer 450 selects a set of operating parameters (step1010). The low level optimizer 450 predicts operating performance (e.g.,power consumption) of the HVAC system operating according to theselected set of operating parameters (step 1020). Moreover, the lowlevel optimizer 450 determines if a different set of values of controlvariables is available (step 1030). The different set of operatingparameters may include one or more values perturbed from the set ofvalues of the control variables. For example, the set of values of thecontrol variables includes “70% chiller capacity, 25 F inlet temperatureand 45 F outlet temperature” and a different set of values of thecontrol variables includes “75% chiller capacity, 25 F inlet temperatureand 45 F outlet temperature.” Responsive to determining that a differentset of values of control parameters is available, the low leveloptimizer 450 selects the different set of values of control parameters,and predicts operating performance of the HVAC system operatingaccording to the different set of values of the control variables.

Responsive to determining that no additional set of values of controlvariables is available (or all operating performances of the HVAC systemfor different sets of values of control variables is obtained), the lowlevel optimizer 450 obtains gradients of the operating performance (step1040). Moreover, the low level optimizer 450 determines sensitivity ofthe control variables on the operating performance based on thegradients (step 1050). For example, the low level optimizer 450 maydetermine that a group of the control variables or a range of values ofthe control variables is sensitive to the operating performance of theHVAC system, responsive to determining that magnitudes of the gradientsof the operating performance (e.g., power consumption) with respect tothe group of the control variables or the range of values of the groupof the control variables being higher than a predetermined threshold.Similarly, the low level optimizer 450 determines that another group ofcontrol variables or a range of values of the other group of controlvariables is insensitive to the operating performance of the HVACsystem, responsive to determining that magnitudes of the gradients ofthe performance with respect to the other group of control variables orthe range of values of the control variables being less than thepredetermined threshold. Additionally or alternatively, the low leveloptimizer 450 determines that another group of control variables or arange of values of the other group of control variables is insensitiveto the operating performance of the HVAC system, responsive todetermining that the range of values of the other group of controlvariables renders values of operating performance of the energy plantwithin a predetermined amount from a local minima.

Configuration of Exemplary Embodiments

The construction and arrangement of the systems and methods as shown inthe various exemplary embodiments are illustrative only. Although only afew embodiments have been described in detail in this disclosure, manymodifications are possible (e.g., variations in sizes, dimensions,structures, shapes and proportions of the various elements, values ofparameters, mounting arrangements, use of materials, colors,orientations, etc.). For example, the position of elements may bereversed or otherwise varied and the nature or number of discreteelements or positions may be altered or varied. Accordingly, all suchmodifications are intended to be included within the scope of thepresent disclosure. The order or sequence of any process or method stepsmay be varied or re-sequenced according to alternative embodiments.Other substitutions, modifications, changes, and omissions may be madein the design, operating conditions and arrangement of the exemplaryembodiments without departing from the scope of the present disclosure.

The present disclosure contemplates methods, systems and programproducts on any machine-readable media for accomplishing variousoperations. The embodiments of the present disclosure may be implementedusing existing computer processors, or by a special purpose computerprocessor for an appropriate system, incorporated for this or anotherpurpose, or by a hardwired system. Embodiments within the scope of thepresent disclosure include program products comprising machine-readablemedia for carrying or having machine-executable instructions or datastructures stored thereon. Such machine-readable media can be anyavailable media that can be accessed by a general purpose or specialpurpose computer or other machine with a processor. By way of example,such machine-readable media can include RAM, ROM, EPROM, EEPROM, CD-ROMor other optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to carry or storedesired program code in the form of machine-executable instructions ordata structures and which can be accessed by a general purpose orspecial purpose computer or other machine with a processor. Combinationsof the above are also included within the scope of machine-readablemedia. Machine-executable instructions include, for example,instructions and data which cause a general purpose computer, specialpurpose computer, or special purpose processing machines to perform acertain function or group of functions.

Although the figures show a specific order of method steps, the order ofthe steps may differ from what is depicted. Also two or more steps maybe performed concurrently or with partial concurrence. Such variationwill depend on the software and hardware systems chosen and on designerchoice. All such variations are within the scope of the disclosure.Likewise, software implementations could be accomplished with standardprogramming techniques with rule based logic and other logic toaccomplish the various connection steps, processing steps, comparisonsteps and decision steps.

What is claimed is:
 1. A controller for an energy plant having heating,ventilation, and air conditioning (HVAC) devices, the controllercomprising: a processing circuit comprising a processor and memorystoring instructions executed by the processor, the processing circuitconfigured to: generate gradient data indicating a gradient of operatingperformance of the energy plant with respect to values of a plurality ofcontrol variables of the HVAC devices; determine, from the plurality ofcontrol variables, a reduced group of control variables of the HVACdevices based on the gradient data; determine a set of values of thereduced group of control variables; and operate the HVAC devices of theenergy plant according to the determined set of values of the reducedgroup of control variables.
 2. The controller of claim 1, wherein thecontrol variables include at least a capacity, temperature, or pressureof one or more of the HVAC devices.
 3. The controller of claim 1,wherein the processing circuit is configured to determine the reducedgroup of control variables by: determining a range of values of acontrol variable, to which the operating performance of the energy plantis insensitive; and excluding the control variable from the plurality ofcontrol variables to obtain the reduced group of control variables. 4.The controller of claim 3, wherein the processing circuit is configuredto: operate the energy plant according to a predetermined value of theexcluded control variable.
 5. The controller of claim 3, wherein theprocessing circuit is configured to: determine a local minimum of theoperating performance of the energy plant, determine a range of valuesof the operating performance of the energy plant, the range of values ofthe operating performance within a predetermined amount from the localminimum, and determine the range of values of the control variablecorresponding to the range of values of the operating performance. 6.The controller of claim 3, wherein the processing circuit is configuredto: determine the range of values of the control variable rendering thegradient below a threshold.
 7. The controller of claim 1, wherein theprocessing circuit is configured to determine the set of values of thereduced group of control variables by: predicting states of the HVACdevices using a non-linear optimizer.
 8. The controller of claim 7,wherein the processing circuit is configured to determine the set ofvalues of the reduced group of control variables by: excluding one ormore control variables to which the operating performance of the energyplant is insensitive from predicting the states of the HVAC devicesusing the non-linear optimizer.
 9. The controller of claim 1, whereinthe processing circuit is configured to: generate the gradient data inresponse to detecting a change in a topology of the HVAC devices.
 10. Amethod of operating an energy plant having heating, ventilation, and airconditioning (HVAC) devices, the method comprising: generating gradientdata indicating a gradient of operating performance of the energy plantwith respect to values of a plurality of control variables of the HVACdevices; determining, from the plurality of control variables, a reducedgroup of control variables of the HVAC devices based on the gradientdata; determining a set of values of the reduced group of controlvariables; and operating the HVAC devices of the energy plant accordingto the determined set of values of the reduced group of controlvariables.
 11. The method of claim 10, wherein the control variablesinclude at least a capacity, temperature, or pressure of one or more ofthe HVAC devices.
 12. The method of claim 10, wherein determining thereduced group of control variables includes: determining a range ofvalues of a control variable, to which the operating performance of theenergy plant is insensitive; and excluding the control variable from theplurality of control variables to obtain the reduced group of controlvariables.
 13. The method of claim 12, further comprising: operating theenergy plant according to a predetermined value of the excluded controlvariable.
 14. The method of claim 12, further comprising: determining alocal minimum of the operating performance of the energy plant,determining a range of values of the operating performance of the energyplant, the range of values of the operating performance within apredetermined amount from the local minimum, and determining the rangeof values of the control variable corresponding to the range of valuesof the operating performance.
 15. The method of claim 12, furthercomprising: determining the range of values of the control variablerendering the gradient below a threshold.
 16. The method of claim 10,further comprising: predicting states of the HVAC devices using anon-linear optimizer.
 17. The method of claim 16, further comprising:excluding one or more control variables, to which the operatingperformance of the energy plant is insensitive from predicting thestates of the HVAC devices using the non-linear optimizer.
 18. Themethod of claim 10, further comprising: generating the gradient data inresponse to detecting a change in a topology of the HVAC devices.
 19. Anon-transitory computer readable medium comprising instructions whenexecuted by a processor cause the processor to: detect a change in atopology of a plurality of heating, ventilation, and air conditioning(HVAC) devices of an energy plant; determine, from a plurality ofcontrol variables of the plurality of HVAC devices, a reduced group ofcontrol variables of the plurality of HVAC devices, in response todetecting the change in the topology of the plurality of HVAC devices;determine a set of values of the reduced group of control variables; andoperate the plurality of HVAC devices the energy plant according to thedetermined set of values of the reduced group of control variables. 20.The non-transitory computer readable medium of claim 19, wherein theinstructions when executed by the processor further cause the processorto: generate gradient data indicating a gradient of operatingperformance of the energy plant with respect to values of the pluralityof HVAC devices, in response to detecting the change in the topology ofthe plurality of HVAC devices, wherein the reduced group of controlvariables are determined based on the gradient data.